With an increasing number of deep learning models being deployed in production, questions regarding data privacy and misuse are being raised [1]}.
The trend of training larger models on more data [2]}, training models becomes increasingly expensive. Especially in a continual learning setting where models get trained over many months or years, they accrue a lot of value and are thus increasingly susceptible to theft.
This prompts for technical solutions to monitor and enforce control over these models [3]}.
